Global Patterns of Freshwater Fish Threatenedness

By Justine Carey and Anna-Lisa Dittrich

Research question

What are the key anthropogenic or land-related factors which most contribute to freshwater fish threatenedness globally?

Introduction to data

• IUCN Red List (12,452 freshwater fish species, 2,678 threatened) species) (IUCN, n.d.)

• HydroSHEDS catchment boundaries - level 6 resolution (16,024) (Lehner and Grill 2013)

Data preparation

• IUCN distribution data was intersected with HydroBASIN catchments in QGIS
• Combined into one dataframe in R
• Duplicate rows deleted, non-native species excluded
• Additional data bound using leftbind etc (do we need/have time for this slide?)

Missing values

vis_miss(data_withNA,warn_large_data = FALSE)

Getting an overview - Maps

Hydrosheds downloaded from https://www.hydrosheds.org/products/hydrobasins and combined with the dataset to one Shapefile.

library(dplyr)
library(sf)
hydrosheds<- st_read("Hydrosheds_all.shp")

Reading layer `Hydrosheds_all' from data source 
  `/Users/justinecarey/Library/Mobile Documents/com~apple~CloudDocs/09_study/BOKU/2023-2024 WS/Exploratory Data Analysis in R/Final/Hydrosheds_all.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 16397 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -55.9875 xmax: 180 ymax: 83.62564
Geodetic CRS:  WGS 84

joined_world <- hydrosheds %>%
  left_join(data_withNA, by=c("HYBAS_ID"="hybas_id"))

Number of species per sub-basin

library(ggplot2)
ggplot()+ 
  theme_void()+ 
  geom_sf(data=joined_world, aes(fill=TotalSppBasin), color=NA)+
  scale_fill_gradient(low="khaki",high="palegreen4", na.value="grey", name="Total species")

Number of threatened species

ggplot()+
  theme_void()+
  geom_sf(data=joined_world, aes(fill=TotalSppThreatBasin), color=NA)+
  scale_fill_gradient(low="dodgerblue2",high="red4", na.value="grey")+
  labs(fill="Total threatened species")

Number of extinct species

joined_world$Extinct <- cut(joined_world$TotalSppExtBasin, 
                            breaks = c(-Inf,c(0,1,2,3),Inf), 
                            labels = c("0","1","2","3","15"),include.lowest=TRUE)
ggplot()+
  theme_void()+
  geom_sf(data=joined_world, aes(fill=Extinct), color=NA)+
  scale_fill_manual(values=c("darkolivegreen3","gold2","orange3","red4"),
                    labels=c("1","2","3","15"))+  labs(fill="Total extinct species")

Getting an overview (simple graphs)

Correlation matrix

data_withNA %>%
  ggpairs(aes(colour = continent), columns = c(8,14,23)) %>%
  ggplotly(tooltip = c("x", "y", "colour")) %>%
  highlight("plotly_selected")

3D scatterplot

threshold_value <- 2.5
filtered_data <- data_withNA %>% filter(dams_riverKM <= threshold_value)

library(dplyr)
different_obs <- anti_join(data_withNA, filtered_data, by = "hybas_id")

custom_colours <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a","yellow")

plot_ly(filtered_data, x = ~CSI_min, y = ~ThreatenedRatio_B, z = ~dams_riverKM,
                color = ~continent, colors = custom_colours) %>%
  add_markers(marker = list(size = 3))

Principal component analysis (PCA)

PCA_nona <- prcomp(data_noNA[,2:41], center=TRUE, scale.=TRUE)
summary(PCA_nona)

Importance of components:
                          PC1     PC2     PC3    PC4     PC5     PC6     PC7
Standard deviation     3.1654 1.74110 1.67662 1.4792 1.42515 1.35322 1.30937
Proportion of Variance 0.2505 0.07579 0.07028 0.0547 0.05078 0.04578 0.04286
Cumulative Proportion  0.2505 0.32628 0.39656 0.4513 0.50204 0.54782 0.59068
                           PC8    PC9    PC10   PC11    PC12    PC13    PC14
Standard deviation     1.24077 1.1593 1.13093 1.0917 1.05912 1.03677 0.96699
Proportion of Variance 0.03849 0.0336 0.03198 0.0298 0.02804 0.02687 0.02338
Cumulative Proportion  0.62917 0.6628 0.69474 0.7245 0.75258 0.77945 0.80283
                          PC15    PC16    PC17    PC18   PC19    PC20    PC21
Standard deviation     0.95175 0.88344 0.83805 0.81661 0.7721 0.76822 0.61540
Proportion of Variance 0.02265 0.01951 0.01756 0.01667 0.0149 0.01475 0.00947
Cumulative Proportion  0.82547 0.84499 0.86254 0.87922 0.8941 0.90887 0.91834
                          PC22    PC23    PC24    PC25    PC26    PC27    PC28
Standard deviation     0.61292 0.56839 0.56071 0.53587 0.51636 0.48128 0.45242
Proportion of Variance 0.00939 0.00808 0.00786 0.00718 0.00667 0.00579 0.00512
Cumulative Proportion  0.92773 0.93581 0.94367 0.95085 0.95752 0.96331 0.96842
                          PC29    PC30    PC31    PC32   PC33    PC34    PC35
Standard deviation     0.42165 0.41572 0.40581 0.38230 0.3740 0.35599 0.31387
Proportion of Variance 0.00444 0.00432 0.00412 0.00365 0.0035 0.00317 0.00246
Cumulative Proportion  0.97287 0.97719 0.98131 0.98496 0.9885 0.99163 0.99409
                          PC36    PC37    PC38   PC39    PC40
Standard deviation     0.27995 0.24990 0.22596 0.1902 0.09194
Proportion of Variance 0.00196 0.00156 0.00128 0.0009 0.00021
Cumulative Proportion  0.99605 0.99761 0.99888 0.9998 1.00000

PCA - Biplot

library(ggfortify)
autoplot(PCA_nona, data=data_noNA, colour ='ThreatenedRatio_B',
         loadings=TRUE, loadings.colours='blue',
         loadings.label=TRUE, loadings.label.size =3)

PCA - Importance of principal components

library(factoextra)
fviz_eig(PCA_nona) 

PCA - Contribution of variables

library(corrplot)
var<-get_pca_var(PCA_nona) #list of matrices containing all results for active variables
corrplot(var$coord, is.corr=FALSE) 

Random forest model

               IncNodePurity
ire_pc_sse         2.0440932
crp_pc_sse         1.8930350
ppd_pk_sav         5.5427870
pop_ct_ssu         4.1981079
ire_pc_use         3.5280902
pst_pc_sse         4.2877228
ppd_pk_uav         6.3457655
pst_pc_use         4.5753773
ero_kh_sav         7.5318707
hft_ix_s09         4.2841666
hft_ix_s93         4.0061011
crp_pc_use         2.6675691
gdp_ud_ssu         2.9039621
ero_kh_uav         7.9507029
pop_ct_usu         3.7881073
hft_ix_u09         3.7826659
urb_pc_sse         0.4743752
gdp_ud_usu         2.6797934
hft_ix_u93         4.2279785
rdd_mk_sav         4.5319660
urb_pc_use         0.5134280
rdd_mk_uav         4.8897086
nli_ix_sav         5.4930418
rev_mc_usu         1.3949523
nli_ix_uav         2.4441400
sum_dams           0.5359298
dams_riverKM       0.7949689
dams_basinArea     0.7624746
riv_tc_ssu         4.9694795
riv_tc_usu         4.6713837
pac_pc_sse         2.8259419
sgr_dk_sav         9.5567625
pac_pc_use         2.9776126
CSI_max            0.1276659
for_pc_use         2.9268159
for_pc_sse         2.7028816
gdp_ud_sav        15.4595145
CSI_mainStem       4.9335657
CSI_min            4.9854327
CSI_wMean          4.1357270

Discussion

References

IUCN. n.d. “The IUCN Red List of Threatened Species.”

Lehner, B., and G Grill. 2013. “Global River Hydrography and Network Routing: Baseline Data and New Approaches to Study the World’s Large River Systems.” Hydrological Processes 27 (15): 21712186.